Abstract
This article presents the results of an experiment in which we investigated how prior algebra knowledge and personality can influence the permanence time from the confusion state to frustration/boredom state in a computer learning environment. Our experimental results indicate that people with a neurotic personality and a low level of algebra knowledge can deal with confusion for less time and can easily feel frustrated/bored when there is no intervention. Our analysis also suggest that people with an extroversion personality and a low level of algebra knowledge are able to control confusion for longer, leading to later interventions. These findings support that it is possible to detect emotions in a less invasive way and without the need of physiological sensors or complex algorithms. Furthermore, obtained median times can be incorporated into computational regulation models (e.g. adaptive interfaces) to regulate students’ emotion during the teaching-learning process.
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Notes
- 1.
As students can feel more than one emotion each time, in this paper we are considering the dominant emotion in a moment in time and the transition to another dominant emotion [18] during the teaching-learning process.
- 2.
Available at https://personalitatem.ufs.br/inventory/home.xhtml.
- 3.
Available at http://acubo.tecnologia.ws/aluno.html.
- 4.
References
Pekrun, R.: The control-value theory of achievement emotions: assumptions, corollaries, and implications for educational research and practice. Educ. Psychol. Rev. 18(4), 315–341 (2006)
Sullins, J., Graesser, A.C.: The relationship between cognitive disequilibrium, emotions and individual differences on student question generation. Int. J. Learn. Technol. 9(3), 221–247 (2014)
D’Mello, S., Graesser, A.: AutoTutor and affective autotutor: learning by talking with cognitively and emotionally intelligent computers that talk back. ACM Trans. Interact. Intell. Syst. 2(4), 23:1–23:39 (2013)
D’Mello, S., Calvo, R.A.: Beyond the basic emotions: what should affective computing compute? In: CHI 2013 Extended Abstracts on Human Factors in Computing Systems, pp. 2287–2294 (2013)
Craig, S., Graesser, A., Sullins, J., Gholson, B.: Affect and learning: an exploratory look into the role of affect in learning with AutoTutor. J. Educ. Media 29(3), 241–250 (2004)
D’Mello, S., Picard, R.W., Graesser, A.: Toward an affect-sensitive AutoTutor. IEEE Intell. Syst. 22(4), 53–61 (2007)
Graesser, A., D’Mello, S.K.: Theoretical perspectives on affect and deep learning. In: Calvo, R., D’Mello, S. (eds.) New Perspectives on Affect and Learning Technologies, vol. 3, pp. 11–21. Springer, New York (2011). https://doi.org/10.1007/978-1-4419-9625-1_2
Shanabrook, D.H., Arroyo, I., Woolf, B.P.: Using touch as a predictor of effort: what the iPad can tell us about user affective state. In: Masthoff, J., Mobasher, B., Desmarais, M.C., Nkambou, R. (eds.) UMAP 2012. LNCS, vol. 7379, pp. 322–327. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31454-4_29
Xiaolan, P., Lun, X., Xin, L., Zhiliang, W.: Emotional state transition model based on stimulus and personality characteristics. China Commun. 10(6), 146–155 (2013)
Gross, J.J.: Emotion regulation: affective, cognitive, and social consequences. Psychophysiology 39(3), 281–291 (2002)
Graesser, A.C., Chipman, P., Haynes, B.C., Olney, A.: AutoTutor: an intelligent tutoring system with mixed-initiative dialogue. IEEE Trans. Educ. 48(4), 612–618 (2005)
D’Mello, S., Lehman, B., Pekrun, R., Graesser, A.: Confusion can be beneficial for learning. Learn. Instr. 29, 153–170 (2014)
Clifford, M.M.: Failure tolerance and academic risk-taking in ten- to twelve-year-old students. Br. J. Educ. Psychol. 58(1), 15–27 (1988)
Dweck, C.S.: Mindset: The New Psychology of Success, 1st edn. Random House Incorporated, New York (2006)
Meyer, D.K., Turner, J.C.: Re-conceptualizing emotion and motivation to learn in classroom contexts. Educ. Psychol. Rev. 18(4), 377–390 (2006)
Pekrun, R., Götz, T., Daniels, L.M., Stupnisky, R.H., Perry, R.P.: Boredom in achievement settings: exploring control-value antecedents and performance outcomes of a neglected emotion. J. Educ. Psychol. 102(3), 531–549 (2010)
D’Mello, S.: Monitoring affective trajectories during complex learning. In: Seel, M. (ed.) Encyclopedia of the Sciences of Learning, pp. 2325–2328. Springer, Boston (2012). https://doi.org/10.1007/978-1-4419-1428-6
Larsen, J.T., McGraw, A.P., Cacioppo, J.T.: Can people feel happy and sad at the same time? J. Pers. Soc. Psychol. 81(4), 684 (2001)
Meeker, W.Q., Escobar, L.A.: Statistical Methods for Reliability Data, 1st edn. Wiley, New York (1998)
Sebe, N., Cohen, I., Gevers, T., Huang, T.S.: Multimodal approaches for emotion recognition: a survey. In: Proceedings of SPIE, vol. 5670, pp. 56–67 (2005)
Lera, E., Garreta-Domingo, M.: Ten emotion heuristics: guidelines for assessing the user’s affective dimension easily and cost-effectively. In: Proceedings of 21st BCS HCI Group Conference, vol. 2, pp. pp. 163–166 (2007)
Kleinbaum, D., Klein, M.: Survival Analysis: A Self-Learning Text, 3rd edn. Springer, New York (2012). https://doi.org/10.1007/978-1-4757-2555-1
Bernardo, J.M., Smith, A.F.M.: Bayesian Theory, 1st edn. Wiley, New York (1994)
Sahu, S.K., Dey, D.K., Aslanidou, H., Sinha, D.: A Weibull regression model with gamma frailties for multivariate survival data. Lifetime Data Anal. 3(2), 123–137 (1997)
Gamerman, D., Lopes, H.F.: Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, 2nd edn. Chapman & Hall/CRC, Boca Raton (2006)
Lunn, D.J., Thomas, A., Best, N., Spiegelhalter, D.: Winbugs - a Bayesian modelling framework: concepts, structure, and extensibility. Stat. Comput. 10(4), 325–337 (2000)
Niederreiter, H.: Some current issues in quasi-Monte Carlo methods. J. Complex. 19(3), 428–433 (2003)
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Reis, H., Alvares, D., Jaques, P., Isotani, S. (2018). Analysis of Permanence Time in Emotional States: A Case Study Using Educational Software. In: Nkambou, R., Azevedo, R., Vassileva, J. (eds) Intelligent Tutoring Systems. ITS 2018. Lecture Notes in Computer Science(), vol 10858. Springer, Cham. https://doi.org/10.1007/978-3-319-91464-0_18
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